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Probabilities and Predictions: Modeling the Development of Scientific Problem-Solving Skills

Ron Stevens^*, David F. Johnson, and Amy Soller^,

^* UCLA IMMEX Project, 5601 W. Slauson Avenue, Suite 255, Culver City, CA 90230 Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA 92697 Automated Reasoning Systems, Institute for Research in Science and Technology (ITC-IRST), Trento, Italy

Address correspondence to: Ron Stevens (immex_ron{at}hotmail.com).

The IMMEX (Interactive Multi-Media Exercises) Web-based problem set platform enables the online delivery of complex, multimedia simulations, the rapid collection of student performance data, and has already been used in several genetic simulations. The next step is the use of these data to understand and improve student learning in a formative manner. This article describes the development of probabilistic models of undergraduate student problem solving in molecular genetics that detailed the spectrum of strategies students used when problem solving, and how the strategic approaches evolved with experience. The actions of 776 university sophomore biology majors from three molecular biology lecture courses were recorded and analyzed. Each of six simulations were first grouped by artificial neural network clustering to provide individual performance measures, and then sequences of these performances were probabilistically modeled by hidden Markov modeling to provide measures of progress. The models showed that students with different initial problem-solving abilities choose different strategies. Initial and final strategies varied across different sections of the same course and were not strongly correlated with other achievement measures. In contrast to previous studies, we observed no significant gender differences. We suggest that instructor interventions based on early student performances with these simulations may assist students to recognize effective and efficient problem-solving strategies and enhance learning.

Key Words: scientific problem-solving strategies • hidden Markov models • learning trajectory • neural networks